Seismic damage assessment of reinforced concrete (RC) structures is a vital issue for post-earthquake evaluation. Conventional onsite inspection depends greatly on subjective judgments and engineering experiences of human inspectors, and the efficiency is limited to large-scale urban areas. This study proposes a computer-vision and machine-learning-based seismic damage assessment framework for RC structures. A refined Park-Ang model is built to express the coupled effects of structural ductility and energy dissipation, which reflects the nonlinear seismic damage accumulation and generates a synthetical seismic damage indicator within 0~1 using hysteretic curve data. A deep neural network is established to regress the damage indicator using damage-related and design-related parameters as inputs. The results show that the correlation coefficients between the predicted and actual seismic damage index exceed 0.98, and the predicted seismic damage index is unbiased and stable without overfitting. Furthermore, the effectiveness, robustness, and generalization ability of the proposed method are verified.
Efficient water vapor removal is important for both building humidity control and industrial gas dehydration, where operating conditions may span broader temperature and pressure ranges. Driven by a pressure gradient, membrane-based dehumidification has emerged as an energy-effic…
To address the existing gap in quantitative evaluation regarding the integrated visual effect of spring water landscapes and street spaces within Historical and Cultural Neighborhoods of Spring Zone, the Jinan Mingfu City area is selected as a typical case for this research. A qu…
Conventional building codes rely on standard dry-state conditions, systematically underestimating thermal degradation induced by wind-driven rain. To address this, this study establishes a dynamic evaluation framework to quantify moisture-induced deviations and re-rank the hygrot…
Illegal waste dumping remains a persistent urban management problem, yet enforcement is often reactive because cities lack spatially precise evidence on where risk is concentrated. This study presents an unsupervised machine learning and urban big data framework that converts rou…
Computer vision technology has emerged as a promising approach for multipoint displacement monitoring of civil infrastructure, owing to its inherent noncontact operation and remote measurement capabilities. However, its measurement accuracy is greatly affected by ambient temperat…
Urban color plays a fundamental role in shaping the visual character and cultural identity of cities. Yet in many contexts, current practices remain fragmented, with color analysis often disconnected from planning implementation and governance. To address this issue, this study p…